ArticleNeuroImage2020
Permutation inference for canonical correlation analysis.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- KSTITCH links cellular morphology and gene expression in spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Disentangling white matter correlates of symptom severity and general impairment in early psychosis.Molecular psychiatry · 2026Article
- Survey Validation and Optimization to Assess Public Awareness of Origin Protected Designation (PDO) Cheeses: Majorero and Palmero Cases.Foods (Basel, Switzerland) · 2026Article
- Exploration of multidimensional associations between craniomaxillofacial and dental arch structures.BMC oral health · 2026Article
- Protracted Maturation of Proactive and Reactive Systems Predicts Cognitive Stability and Psychopathology : A longitudinal multi-cohort study.bioRxiv : the preprint server for biology · 2026Article
- Multivariate relationships between social cognitive performance and functional connectivity during task and rest across schizophrenia spectrum disorders and healthy controls.Molecular psychiatry · 2026Article
- Widespread synaptic density loss in schizophrenia follows molecular and network architecture.Molecular psychiatry · 2026Article
- Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.Nature communications · 2026Article
- Regularized canonical correlation models improve prediction of weather impacts on semen quality in Murciano-Granadina bucks.Veterinary and animal science · 2026Article
- Latent brain state dynamics predict early amyloid accumulation and cognitive impairment.bioRxiv : the preprint server for biology · 2026Article
- Exploring Links between Brain Image-Derived Phenotypes and Accelerometer-Measured Physical Activity in the UK Biobank.bioRxiv : the preprint server for biology · 2026Article
- Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study.Brain communications · 2026Article
- Multilevel analysis of salt stress responses in sorghum during seed germination.Frontiers in plant science · 2026Article
- Relations between sleep quality and academic performance among Vietnamese high-school students.Frontiers in psychology · 2026Article
- Leveraging multivariate approaches to advance the science of early-life adversity.Child abuse & neglect · 2025Review
- Multivariate Links Between the Developmental Timing of Adversity Exposure and White Matter Tract Connectivity in Adulthood.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Cellular basis for cortical network aging in primates.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.
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